System and method for electric load identification and classification employing support vector machine

ABSTRACT

A method identifies electric load types of a plurality of different electric loads. The method includes providing a support vector machine load feature database of a plurality of different electric load types; sensing a voltage signal and a current signal for each of the different electric loads; determining a load feature vector including at least six steady-state features with a processor from the sensed voltage signal and the sensed current signal; and identifying one of the different electric load types by relating the load feature vector including the at least six steady-state features to the support vector machine load feature database.

This invention was made with Government support under DE-EE0003911awarded by the Department of Energy National Energy TechnologyLaboratory. The Government has certain rights in this invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to commonly assigned, copending U.S. patentapplication Ser. No. 13/304,783, filed Nov. 28, 2011, entitled “SystemAnd Method Employing A Hierarchical Load Feature Database To IdentifyElectric Load Types Of Different Electric Loads”;

U.S. patent application Ser. No. 13/304,758, filed Nov. 28, 2011,entitled “System And Method Employing A Self-Organizing Map Load FeatureDatabase To Identify Electric Load Types Of Different Electric Loads”;and

U.S. patent application Ser. No. 13/304,834, filed Nov. 28, 2011,entitled “System And Method Employing A Minimum Distance And A LoadFeature Database To Identify Electric Load Types Of Different ElectricLoads”.

BACKGROUND

Field

The disclosed concept pertains generally to electric loads and, moreparticularly, to methods of identifying electric load types of electricloads. The disclosed concept also pertains to systems for identifyingelectric load types of electric loads.

Background Information

Electric loads in residential and commercial building sectors accountfor over 75% of the total electricity consumption in the United Statesin 2009. Electric loads are commonly divided into several groups, suchas for example and without limitation, space conditioning loads, waterheating loads, ventilation loads, major appliances, lighting loads, andmiscellaneous electric loads (MELs). MELs are the wide and diversecollection of portable and plug-in electricity-consuming devices alongwith all hard-wired electric loads that do not fit into othercategories. TV sets and accessories, computers and accessories, portableequipment using chargers, and kitchen appliances are examples of typicalMELs. Compared with any other single major category, MELs currentlyconsume the largest portion of electricity. A recent report from theU.S. Department of Energy (DoE) predicts that MELs consumption willincrease by an average of 2.3 percent per year and, in 2035, willaccount for 40 percent of the total electricity consumption in thecommercial sector.

MELs' relatively large portion in electricity consumption leads toincreasing needs and opportunities of energy management and saving. Arecent U.S. DoE research program, called Building America, aims at 50%energy savings in new homes by 2015 and 100% savings (zero net energy)by 2020. This program has started to identify and develop advancedsolutions that can significantly reduce MELs' power consumption.Moreover, granular load energy consumption and performance informationis desired to accelerate the path toward smarter building energyintensity reduction, demand response, peak shaving, and energyoptimization and saving.

MELs present special challenges because their operations are mainlyunder the need and control of building occupants. Without advancedcontrol and management, MELs can constitute 30% to 40% of theelectricity use in homes. Furthermore, MELs are distinct from other loadcategories as many MELs are of notable importance in daily life. Forinstance, a circuit protection device on an uninterruptable power supply(UPS) or at the input to a power strip interrupts all downstream powercircuits when an overcurrent fault happens, but such an unexpected powerinterruption will cause sensitive equipment, such as a plugged-indesktop computer, to lose all current memory-based work.

MELs currently consume more electricity than any other single end-useservice. MELs provide granular energy consumption and performanceinformation to meet rising needs and opportunities of energy saving,demand response, peak shaving, and building management. A reliableintelligent method and system to identify different MELs is desired.

Several methods have been proposed to non-intrusively identify electricloads. Such known methods mainly consist of two major categories.Steady-state features, such as instantaneous real power, power factor,V-I trajectory and harmonic components, are extracted from voltage andcurrent measurements. In the first category, some methods compare thesefeatures and their variations with a predefined database. In the secondcategory, some methods adopt computational intelligent (CI) algorithms,such as radial basis functions (RBF), particle swarm optimization (PSO)and artificial neural networks (ANN).

The former category has disadvantages in accuracy, robustness andapplicability. For instance, different MELs with similar front-end powersupply units are not distinguishable in this manner. Also, few of theknown methods are specifically designed for MELs. As a result, productson market, such as Navetas™ and enPowerMe™, are restricted to only alimited number of MELs.

The latter category suffers from the lack of knowledge during trainingand computational cost, which limits its applicability.

Moreover, the rapid development of power supply designs bringschallenges to load identification. One type of MEL may be supplied bydifferent power supply topologies. Therefore, a reliable loadidentification algorithm should be able to identify loads with diversespecifications, such as manufacturer and power rating.

A support vector machine (SVM) is a well known concept in computerscience for a set of related supervised learning methods that analyzedata and recognize patterns, used for classification and regressionanalysis. A SVM has discriminative power for static classificationproblems and the capability to construct flexible decision boundaries.The standard SVM takes a set of input data made up of training examplesand predicts, for each given input example, which of two possibleclasses the input belongs. This makes the SVM a non-probabilistic binarylinear classifier. Given a set of training examples, each marked asbelonging to one of two categories, an SVM training algorithm builds amodel that assigns the examples into one category or the other. An SVMmodel is a representation of the examples as points in space, mapped sothat the examples of the separate categories are divided by a clear gapthat is as wide as possible. New examples are then mapped into that samespace and predicted to belong to one of the categories based on whichside of the gap they fall on.

A SVM constructs a hyperplane or set of hyperplanes in a high- orinfinite-dimensional space, which can be used for classification,regression, or other tasks. Intuitively, a good separation is achievedby the hyperplane that has the largest distance to the nearest trainingdata points of any class (so-called functional margin), since in generalthe larger the margin the lower the generalization error of theclassifier.

Whereas an original problem may be stated in a finite dimensional space,called the original space, it often happens that the sets to be mappedare not linearly separable in that space. For this reason, the originalfinite-dimensional space is mapped into a much higher-dimensional space,making the separation easier in that space. To keep the computationalload reasonable, the mapping used by SVM schemes are designed to ensurethat dot products may be computed easily in terms of the variables inthe original space, by defining them in terms of a kernel functionK(x,y) selected to suit the problem. The hyperplanes in the higherdimensional space are defined as the set of points whose inner productwith a vector in that space is constant. The vectors defining thehyperplanes can be chosen to be linear combinations with parametersα_(i) of images of feature vectors that occur in the database. With thischoice of a hyperplane, the points x in the feature space that aremapped into the hyperplane are defined by the relation:

${\sum\limits_{i}^{\;}\;{\alpha_{i}{K\left( {x_{i},x} \right)}}} = {constant}$

If K(x,y) becomes small as y grows further from x, then each element inthe sum measures the degree of closeness of the test point x to thecorresponding database point x_(i). In this way, the sum of kernelsabove can be used to measure the relative nearness of each test point tothe data points originating in one or the other of the sets to bediscriminated. The set of points x mapped into any hyperplane can bequite convoluted as a result allowing much more complex discriminationbetween sets which are not convex at all in the original space.

In the case of support vector machines (SVMs), a data point is viewed asa p-dimensional vector (a list of p numbers), and the goal is toseparate such points with a (p−1)-dimensional hyperplane. This is calleda linear classifier. There are many hyperplanes that might classify thedata. One reasonable choice as the best hyperplane is the one thatrepresents the largest separation, or margin, between the two classes.The hyperplane is chosen so that the distance from it to the nearestdata point on each side is maximized. If such a hyperplane exists, thenit is known as the maximum-margin hyperplane and the linear classifierit defines is known as a maximum margin classifier; or equivalently, theperception of optimal stability.

There is room for improvement in methods of identifying electric loadtypes of a plurality of different electric loads.

There is also room for improvement in systems for identifying electricload types of a plurality of different electric loads.

SUMMARY

These needs and others are met by embodiments of the disclosed concept,which employ support vector machine (SVM) based identification formiscellaneous electric loads and apply either only SVM, or a combinationof SVM and a supervised self-organizing map (SSOM).

In accordance with one aspect of the disclosed concept, a method ofidentifying electric load types of a plurality of different electricloads comprises: providing a support vector machine load featuredatabase of a plurality of different electric load types; sensing avoltage signal and a current signal for each of the different electricloads; determining a load feature vector including at least sixsteady-state features with a processor from the sensed voltage signaland the sensed current signal; and identifying one of the differentelectric load types by relating the load feature vector including the atleast six steady-state features to the support vector machine loadfeature database.

The method may comprise training the support vector machine load featuredatabase as a multi-class one-against-all support vector machine foreach of a plurality of different load classes or for each of thedifferent electric load types.

The method may employ the at least six steady-state features selectedfrom the group consisting of RMS current value, displacement powerfactor, total harmonic distortion of current, power factor, currentcrest factor, current K-factor, admittance, and normalized current thirdand fifth harmonics.

As another aspect of the disclosed concept, a method of identifyingelectric load types of a plurality of different electric loadscomprises: providing a database including a first layer formed by asupervised self-organizing map database and a second layer formed by asupport vector machine database; clustering a plurality of differentload classes having a plurality of different load features in the firstlayer; providing a plurality of different electric load types under eachof the different load classes in the second layer; placing differentones of the different electric load types having similar load featurevectors into a same one of the different load classes; sensing a voltagesignal and a current signal for each of the different electric loads;determining a load feature vector including a plurality of steady-statefeatures with a processor from the sensed voltage signal and the sensedcurrent signal; and identifying by a support vector machine one of thedifferent electric load types by relating the determined load featurevector including the steady-state features in the second layer of thedatabase.

The method may further comprise: training the supervised self-organizingmap database employing data corresponding to the different electric loadtypes; training the support vector machine database as a multi-classone-against-all support vector machine for each of the plurality ofdifferent load classes; identifying the one of the different electricload types as being in one of the plurality of different load classeswith the supervised self-organizing map database; and identifying theone of the different electric load types with the trained support vectormachine database for the one of the plurality of different load classes.

The method may extract information from the trained support vectormachine database and store simplified information in a trained neurongrid; employ as the different load classes a plurality of different loadcategories; determine one of the different load categories employing thedetermined load feature vector; and employ a support vector machinediscriminator function for each the different load categories toidentify the one of the different electric load types.

As another aspect of the disclosed concept, a system for identifyingelectric load types of a plurality of different electric loadscomprises: a database including a first layer formed by a supervisedself-organizing map database and a second layer formed by a supportvector machine database, a plurality of different load classes having aplurality of different load features being clustered in the first layer,a plurality of different electric load types being under each of thedifferent load classes in the second layer, different ones of thedifferent electric load types having similar load feature vectors beingplaced into a same one of the different load classes; a plurality ofsensors structured to sense a voltage signal and a current signal foreach of the different electric loads; and a processor structured todetermine a load feature vector including a plurality of steady-statefeatures from the sensed voltage signal and the sensed current signal,and identify by a support vector machine one of the different electricload types by relating the determined load feature vector including thesteady-state features in the second layer of the database.

The processor may be further structured to train the supervisedself-organizing map database employing data corresponding to thedifferent electric load types, train the support vector machine databaseas a multi-class one-against-all support vector machine for each of theplurality of different load classes, identify the one of the differentelectric load types as being in one of the plurality of different loadclasses with the supervised self-organizing map database, and identifythe one of the different electric load types with the trained supportvector machine database for the one of the plurality of different loadclasses.

The processor may be further structured to extract information from thetrained support vector machine database and store simplified informationin a trained neuron grid, employ as the different load classes aplurality of different load categories; determine one of the differentload categories employing the determined load feature vector, and employa support vector machine discriminator function for each the differentload categories to identify the one of the different electric loadtypes.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding of the disclosed concept can be gained from thefollowing description of the preferred embodiments when read inconjunction with the accompanying drawings in which:

FIG. 1 is a diagram of a support vector machine (SVM) frameworkemploying kernel functions.

FIGS. 2A and 2B are plots of voltage and current profiles, respectively,of a DVD player.

FIGS. 2C and 2D are plots of voltage and current profiles, respectively,of an LCD TV.

FIGS. 2E and 2F are plots of voltage and current profiles, respectively,of an oscillating fan.

FIGS. 3A and 3B are plots of an output grid of a supervisedSelf-Organizing Map (SSOM) using a set of six example steady-statefeatures and harmonics as features, respectively.

FIG. 4 is a block diagram of a cross validation mechanism for testingperformance of a one-against-all SVM identifier in accordance with anembodiment of the disclosed concept.

FIG. 5 is a block diagram of a support vector machine (SVM) based loadclassification/identification system in accordance with embodiments ofthe disclosed concept.

FIG. 6 is a flowchart of a hybrid SSOM/SVM classifier process inaccordance with an embodiment of the disclosed concept.

FIG. 7 is a block diagram of a self-organizing map (SSOM)/support vectormachine (SVM) based load classification/identification system inaccordance with embodiments of the disclosed concept.

FIGS. 8A and 8B are plots of voltage and current profiles, respectively,of an LCD TV used by a trained SSOM in accordance with an embodiment ofthe disclosed concept.

FIGS. 8C and 8D are plots of voltage and current profiles, respectively,of an LED TV used by a trained SSOM in accordance with an embodiment ofthe disclosed concept.

FIGS. 8E and 8F are plots of voltage and current profiles, respectively,of a plasma TV used by a trained SSOM in accordance with an embodimentof the disclosed concept.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

As employed herein, the term “number” shall mean one or an integergreater than one (i.e., a plurality).

As employed herein, the term “processor” shall mean a programmableanalog and/or digital device that can store, retrieve, and process data;a computer; a workstation; a personal computer; a microprocessor; amicrocontroller; a microcomputer; a central processing unit; a mainframecomputer; a mini-computer; a server; a networked processor; or anysuitable processing device or apparatus.

The disclosed concept provides a support vector machine (SVM) basedadvanced identification system and method for miscellaneous electricloads (MELs). Embodiments including applying only SVM as well as acombination of SVM and a supervised self-organizing map (SSOM) aredisclosed. This applies the supervised SSOM to classify and identifyMELs. A relatively large number of MELs are classified into severalclusters. Within each cluster, a SVM can be applied to separate MELswith similar but not identical features.

In the second embodiment, SSOM first clusters a relatively large numberof MELs into several classes. MELs with similar feature values fall intothe same class. SVM is then employed to identify similar MELs. Thecombination of SVM and SSOM shows satisfactory accuracy in tests usingreal-world data. The hybrid SSOM/SVM identifier can achieve betterperformance in the sense of accuracy, robustness and applicability. TheSSOM identifier first extracts information from a relatively largeamount of training data and stores that simplified information in atrained neuron grid. When an input feature vector is presented to thehybrid SSOM/SVM identifier, it first determines which load category itfalls into and then utilizes an SVM discriminator function for eachcategory to make an identification decision.

Multi-Class Support Vector Machine (SVM) Classifier

A method for multi-class classification is known from basic SVMs. In thedisclosed concept, a MEL identification method employs a SVM based loadfeature database formed from sensed current and voltage signals. BasicSVMs are inherently designed for classification of two classes, ω₁ andω₂, of patterns which are described by feature vectors extracted fromdata in a predefined manner. If x is such a feature vector, then the SVMutilizes a (typically nonlinear) mapping from an input feature vectorspace to a high-dimensional (possibly infinite-dimensional) Euclideanspace H, as shown by Equation 1.Φ:x∈R ^(l)→Φ(x)∈H   (Eq. 1)wherein:

x is a feature vector of dimension l;

R is the standard notation of the vector space of real numbers;

R^(l) is the vector space of real numbers (of dimension l); and

Φ is a mapping function, mapping from the l-dimension space of realnumbers to the space H.

In turn, the two classes can be satisfactorily separated by a hyperplaneas defined by Equation 2.g(x)=ω^(T) x+ω ₀   (Eq. 2)wherein:

ω and ω_(o) are co-efficients defining the hyperplane g(x);

superscript T denotes the transpose of a vector; and

g(x) is a function describing a hyperplane in high dimensional space.

Both x and ω, for example, are 2-by-1 vectors, then ω^(T) is ofdimension 1-by-2, and g(x) is a straight line in two-dimensional space.

Once an optimal hyperplane (ω, ω₀) has been determined, classificationof which class an unknown feature vector x* (i.e., a different vectorother than x) belongs to is performed based on the sign of g(x*). TheSVM training algorithm only depends on the training data through innerproducts in H (i.e., on functions of the form shown in Equation 3).K(x _(i) ,x _(j))=

Φ(x _(i)),Φ(x _(j))

   (Eq. 3)wherein:

K is usually called a kernel function; it is common that an SVM needsonly to specify K before its training instead of knowing the explicitform of Φ;

x_(i), i=1, 2, . . . , are feature vectors in the training data; foreach x_(i) denote the corresponding class indicator by y_(i) (+1 for ω₁and −1 for ω₂); and

the function K needs two inputs, thus there is another index besides i,that index is j.

Once an appropriate kernel has been adopted, the optimal hyperplane (ω,ω₀) can be determined from Equation 4:

$\begin{matrix}{\max\limits_{\lambda}\left( {{\sum\limits_{i}^{\;}\;\lambda_{i}} - {\frac{1}{2}{\sum\limits_{i,j}^{\;}\;{\lambda_{i}\lambda_{j}y_{i}y_{j}{K\left( {x_{i},x_{j}} \right)}}}}} \right)} & \left( {{Eq}.\mspace{14mu} 4} \right)\end{matrix}$subject to Equation 5:

$\begin{matrix}{{{0 \leq \lambda_{i} \leq C},{{\sum\limits_{i}^{\;}{\lambda_{i}y_{i}}} = 0}}{{i = 1},2,{\ldots\mspace{14mu} N}}} & \left( {{Eq}.\mspace{14mu} 5} \right)\end{matrix}$wherein:

λ is the vector of nonnegative Lagrange multipliers λ_(i);

C is a parameter to be chosen by the user with a relatively larger Ccorresponding to assigning a relatively higher penalty to errors; and

N is the total number of Lagrange multipliers, N=l, i.e., the dimensionof feature vector x.

The resulting classifier assigns x to ω₁ (ω₂) if Equation 6 is met.

$\begin{matrix}{{g(x)} = {{{\sum\limits_{i = 1}^{N}\;{\lambda_{i}y_{i}{K\left( {x_{i},x} \right)}}} + \omega_{0}} > {( < )0}}} & \left( {{Eq}.\mspace{14mu} 6} \right)\end{matrix}$

FIG. 1 depicts a SVM framework 2 that employs kernel functions, K(.,.)4, 6, 8, where each x_(i) 10 is an input to one of the kernel functions,each λ_(i)y_(i) 12 is a weight to the output of one of the functions,and ω₀ 14 is the bias.

For multi-class SVM kernel selection, which is an M-class problem,common extensions are to either consider it as a set of M two-classproblems (one-against-all) or train M(M−1)/2 basic SVM classifiers(one-against-one). In the disclosed concept, the one-against-alltechnique is employed. For each ω_(i) of the M classes, theone-against-all SVM aims at determining an optimal discriminatorfunction, g_(i)(x), i=1, 2, . . . , M, so that g_(i)(x)>g_(j)(x) for allj≠i and x∈ω_(i).

The classification rule is then defined by Equation 7.

$\begin{matrix}{{x \in \omega_{i}}{{{if}\mspace{14mu} i} = {\arg\;{\max\limits_{k}\left\{ {g_{k}(x)} \right\}}}}} & \left( {{Eq}.\mspace{14mu} 7} \right)\end{matrix}$wherein:

k is an index other than i, since here i is the output number, and kdenotes the index during the searching of such an output i.

Many kernels are available for SVM, such as polynomials, RBF, andhyperbolic tangent. In the disclosed concept, the Gaussian RBF kernel isemployed, which is also the most commonly adopted kernel in patternrecognition problems.

Electric Load Identification Framework Using Only SVM

Known proposals apply SVM to identify harmonic sources. The featuresused in these proposals are high frequency harmonic components.

For the purpose of MELs identification, a different set of steady-statefeatures with practical meaning are adopted with the voltage (V(t)) andcurrent (I(t)) waveforms represented by Fourier series of the form shownby Equations 8 and 9, respectively.

$\begin{matrix}{{V(t)} = {\sum\limits_{k = 1}^{\infty}\;{V_{k}{\sin\left( {{k\;\omega_{0}t} + \delta_{k}} \right)}}}} & \left( {{Eq}.\mspace{14mu} 8} \right) \\{{I(t)} = {\sum\limits_{k = 1}^{\infty}\;{I_{k}{\sin\left( {{k\;\omega_{0}t} + \theta_{k}} \right)}}}} & \left( {{Eq}.\mspace{14mu} 9} \right)\end{matrix}$wherein:

ω₀ is frequency;

k is an index of the order of harmonics; and

δ_(k), θ_(k) are the phase angles of the k-th order harmonic.

The following six steady-state features are considered.

First, the RMS current value, I_(RMS), gives equivalent informationabout the average power.

Second, the average displacement power factor is shown by Equation 10.pf _(disp)=cos(δ₁−θ₁)   (Eq. 10)wherein:

pf_(disp) is the displacement power factor; and

(δ₁−θ₁) is the fundamental power factor angle.

Third, the average total harmonic distortion (THD) of current (THD_(i))is shown by Equation 11.

$\begin{matrix}{{THD}_{I} = {\frac{\sqrt{\sum\limits_{k = 2}^{\infty}I_{k}^{2}}}{I_{1}} \times 100\%}} & \left( {{Eq}.\mspace{14mu} 11} \right)\end{matrix}$wherein:

k is an index of the order of harmonics;

I_(k) is the k-th order harmonic in current; and

I₁ is the fundamental, i.e., first order harmonic.

Fourth, the average power factor (pf) is determined by calculatingdisplacement power factor and the current THD using the fast Fouriertransform (FFT) of the current waveform as shown by Equation 12.

$\begin{matrix}{{pf} = \frac{{pf}_{disp}}{\sqrt{1 + {THD}_{1}^{2}}}} & \left( {{Eq}.\mspace{14mu} 12} \right)\end{matrix}$

Fifth, the crest factor (cf) or peak-to-average ratio (PAR) isdetermined by Equation 13.

$\begin{matrix}{{cf} = \frac{I_{peak}}{I_{RMS}}} & \left( {{Eq}.\mspace{14mu} 13} \right)\end{matrix}$wherein:

I_(peak) is the current's peak amplitude; and

I_(rms) is the current's RMS value.

The example current crest factor or PAR or peak-to-RMS power ratio(PAPR) is a measurement of a waveform, calculated from the peakamplitude of the waveform divided by the RMS value of the waveform. Itis therefore a dimensionless quantity. Crest factor can be used todetect the existence of a current pulse. A sharp peak corresponds to arelatively higher value of crest factor.

Sixth, there are the normalized 3^(rd) (and 5^(th)) harmonics ofcurrent.

The fact that the above example set of steady-state features is betterfor the purpose of MELs identification can be validated by both SVM andSSOM. For three example MELs, a DVD player (D), an LCD TV (T) and aoscillating fan (F), their voltage profiles 22,26,30 and currentprofiles 24,28,32 are shown in FIGS. 2A-2F.

FIGS. 3A-3B show output grids 42,44 of SSOM using two sets of differentfeatures: (1) the disclosed set of six example steady-state features,and (2) harmonics as features. The SSOM output grid 42 gives a betterclustered grid using the disclosed set of steady-state features. ForSVM, unlike SSOM, the training output is parameters describing ahigh-dimensional hyper-plane, which cannot be visualized in twodimensions.

Example 1

Therefore, the advantage of the disclosed set of steady-state featuresover using harmonics is shown by tests as summarized in Table 1. In thisexample, the total number of available feature vectors for training andtesting is 3600, and three different cases are tested and compared. Theresults are generated by solving multi-class one-against-all SVMs. Thiscompares testing success rate of different features sets using amulti-class one-against-all SVMs.

TABLE 1 Success rate 270 points 540 points 1080 points for training, fortraining, for training, and 3330 and 3060 and 2520 points points pointsfor testing for testing for testing Disclosed   100%   100%   100% setof six example steady- state features Harmonics 99.56% 99.53% 99.43% asfeaturesIt is clear to see from Table 1 that simply using harmonics cannotguarantee a 100% success rate even with only three relatively distinctMELs.

Data was collected, processed and employed from commercially availableMELs 48. Steady-state features of an example set of 42 types of MELs,with 5 to 7 brands per type, were evaluated. For the purpose of accuracyand convenience for FFT, the sampling frequency of the data acquisition(DAQ) system was set to 30.72 kS/sec. Lower sampling frequencies, suchas 7.68 kS/sec and 3.84 kS/sec, were also tested with the crossvalidation results being relatively the same. Similar to a known SSOMclassifier, a cross validation mechanism tested the performance of theone-against-all SVM identifier 50, as shown in FIG. 4. This shows theprocess of how to cross-validate the performance of a one-against-allSVM classifier 58. The collected data 52 is divided into two sets:training 54 and testing 56. The SVM classifier 58 is first trained bythe training data and then performs identification 60 on the testingdata. The results 62,64 are compared with the actual labeling of thetesting data to evaluate the success rate 66 and the performance of theSVM identifier 50.

Referring to FIG. 5, a system 70 provides V/I waveform measurement 72,high-dimensional feature extraction 74, a multi-class one-against-allSVM 76 employing load feature clustering by load types 78, an SVMclassifier construction 80, and load identification results 82. Thesystem 70 also includes a processor 84 structured to determine at leastsix different load features from a sensed voltage signal and a sensedcurrent signal for a corresponding one of a plurality of differentelectric loads, and identify, at 82, a load type of the differentelectric load types by relating the different load features to the SVMdatabase 76.

Hybrid SSOM/SVM Classifier

The self-organizing map (SOM) is an unsupervised artificial neuralnetwork that is trained using competitive learning. That is, all neuronscompete for the right to respond to the input data but only one neuronwill win at a time. The training result of a basic SOM is alow-dimensional (typically two-dimensional), discretized grid of neuronswith similar characteristics as the training samples. MELs that aresimilar or share common features in the input space are mapped toneurons that are positioned close to one another to form a cluster 90,as shown in FIG. 3A. With a relatively large number of MELs types, theoutput grid of the SSOM would get very crowded and thus unreadable, asshown in the output grid 44 of FIG. 3B.

For the purpose of MELs identification, different types of loads withsimilar power supply units or features are partitioned into the samecluster. For example, DVD players and set-top boxes are very similar inboth front-end power supply units and steady-state operatingcharacteristics. Therefore, in a trained SSOM, DVD players and set-topboxes are classified into one cluster. However, the disadvantage is thatit is difficult for a SSOM classifier to distinguish DVD players fromset-top boxes. The SVM classifier 58 (FIG. 4), on the other hand,performs well when handling similar, but not identical, sets of featurevectors. Therefore, a hybrid SSOM/SVM achieves better performance thanonly the SSOM classifier or only a multi-class one-against-all SVMclassifier.

Based on front-end electronic circuit topologies of MELs, electricaloperation principles or the functional nature of MELs, and user usagebehaviors, MELs are divided into seven example load categories withdistinct steady-state features: resistive loads (R); reactivepredominant loads (X); electronic loads with power factor correctioncircuit (P); electronic loads without power factor correction circuit(NP); linear power supply using transformer to boost voltage (T); phaseangle controllable loads (PAC); and complex structures (M). For exampleand without limitation, different electric load types having distinctsteady-state features, such as resistive appliances, motor drivenappliances, electronically fed appliances, non-linear loads with directAC connections, and other unknown load types can be employed.

The architecture of the hybrid SSOM/SVM classifier 100 is shown in FIG.6. First, MELs data are collected to form a database at 102. Then, theSSOM is trained with all available MELs data to form a clustered grid,and a multi-class one-against-all SVM is trained for each of the sevenexample load categories at 104. Next, an unknown MEL is identified asbeing one of the seven example load categories at 106. Finally, usingthe trained SVM for the identified load category, the MEL type 109 isidentified at 108.

Referring to FIG. 7, a system 110 includes a processor 111, V/I waveformmeasurement 112, high-dimensional feature extraction 114, a hybridhierarchical SSOM/SVM 116 including a top-layer SSOM 118 providing loadfeature clustering by load categories 119 and a second-layer SVM 120providing load feature clustering by load type under each load category121, SSOM/SVM classifier construction 122, and load identificationresults 124. For example and without limitation, the steady-statefeatures of the determined load feature vector, as part of thehigh-dimensional feature extraction 114, can include voltage-currenttrajectory features selected from the group consisting of area,eccentricity, and Hausdorff distance.

Area, A, refers to the area enclosed by a V-I trajectory. Area isproportional to the magnitude of the phase shift between the voltage andthe current. If current leads voltage, then Area has a positive sign. Ifcurrent lags voltage, then Area becomes negative. Area is directlycalculated from the coordinates of the voltage and current points,(x_(i), y_(i)), on the V-I trajectory.

Eccentricity, E, is the measure of the aspect ratio of a shape, and isthe ratio of the length of the major axis to the length of the minoraxis. This feature helps to identify the shape of a voltage or currentwaveform.

The Hausdorff distance, or Hausdorff metric, also calledPompeiu-Hausdorff distance, measures how far two subsets of a metricspace are from each other. It turns the set of non-empty compact subsetsof a metric space into a metric space in its own right. The Hausdorffdistance is the longest distance one can be forced to travel by anadversary who chooses a point in one of the two sets, from where youthen must travel to the other set. In other words, it is the farthestpoint of a set that you can be to the closest point of a different set.

Example 2

Table 2 summarizes several tests to show the performance of thedisclosed method and system 110 for MELs identification, including thetesting success rate. These test the performance of the multi-classone-against-all SVM classifier 58 of FIG. 4 on a relatively large numberof MELs including a selection of typical MELs.

TABLE 2 Identification MELs Success Rate (%) Compact 98.67 FluorescentLights Fluorescent 100.00 Lights Incandescent 100.00 Lights Fan 100.00Printer 99.66 Cellphone 100.00 Charger DVD player 98.66 Heater 100.00LCD TV 99.72 LED TV 93.33 Microwave 100.00 Plasma TV 89.66 Set Top Box100.00

Example 3

The multi-class one-against-all SVM classifier 58 of FIG. 4 is comparedagainst SSOM classifier performance with different amounts of data. Oneadvantage of the disclosed multi-class one-against-all SVM classifier 58is that it employs a relatively small amount of training data comparedwith other classifiers. Some test results containing three loads anddifferent choices of cross-validation are shown in Table 3, in which theratios between training data and testing data are indicated in the firstcolumn. The following tests are carried out when both classifiers aretrained by feature vectors from 32 different models of 12 types of MELs,with 3600 feature points for each model. For all of the tests disclosedherein, 512-point FFTs are done to calculate the features.

TABLE 3 Success Rate DVD TV Fan Total SVM   100% 93.77%   100% 97.92%(5% for training, 95% for test) SVM   100% 93.43%   100% 97.81% (10% fortraining, 90% for test) SVM   100%   100%   100%   100% (20% fortraining, 80% for test) SVM   100%   100%   100%   100% (30% fortraining, 70% for test) SSOM 49.36% 81.99% 97.34% 76.23% (5% fortraining, 95% for test) SSOM 91.30% 87.81% 97.69% 92.26% (10% fortraining, 90% for test) SSOM 96.74% 94.03% 93.92% 94.90% (20% fortraining, 80% for test) SSOM 99.75% 99.83% 98.00% 99.19% (67% fortraining, 33% for test)

From Table 3, it is clear to see that the multi-class one-against-allSVM classifier 58 can get a 100% testing success rate with only 20% ofthe total data, which is much better than a SSOM classifier.

Example 4

The following tests are for the hybrid SSOM/SVM classifier 100 of FIG.6. These tests are carried out with the SSOM 118 (FIG. 7) being trainedby feature vectors from 32 different models of 12 types of MELs, with3600 feature points for each model. In this case, the output grid of theSSOM 118 gets relatively very crowded and unreadable. Therefore, it isnot shown. In the trained SSOM 118, MELs with similar features aremapped into the same cluster. The voltage profiles 132,136,140 andcurrent profiles 134,138,142 of three types of TVs (e.g., LCD, LED andplasma) that would be clustered together are shown in FIGS. 8A-8F. Thesethree MELs share similar front-end power supply units as well as featurevectors, and thus it is very difficult to distinguish between them.

The different types of TVs are grouped into one cluster by the SSOMclassifier. However, the SSOM classifier gets an average success ratearound only about 85% to identify each type of TV. In contrast, withinthe hybrid SSOM/SVM classifier 100, the average testing success rate isgreater than 95%. These success rates, for similar MELs, are shown inTable 4.

TABLE 4 Success Rate LCD TV LED TV Plasma TV Average SSOM 80.17% 97.85%85.25% 85.28% identifier Hybrid 98.30% 78.89% 98.96% 92.05% SSOM/SVMidentifier (20% data for training) Hybrid 95.99% 90.95% 98.85% 95.26%SSOM/SVM identifier (30% data for training)

From Table 4, it is clear to see that the more training data for the SVM120 in the hybrid SSOM/SVM identifier 116, the better performance ithas. But the SVM training in the hybrid SSOM/SVM identifier 116 requiresfar less data than a pure SSOM classifier.

Compared with methods, such as only SSOM or only SVM classifiers, thedisclosed hybrid SSOM/SVM identifier 116 provides better performance inthe sense of accuracy, robustness and applicability. The SSOM identifier118 first extracts information from the relatively large amount oftraining data and stores that simplified information in the trainedneuron grid. When an input feature vector is presented to the hybridSSOM/SVM identifier 116, it first determines which load category itfalls into, and then employs an SVM discriminator function 120 for eachcategory to get a robust and correct identification decision.

The disclosed concept provides a hybrid SSOM/SVM classifier 116 for thepurpose of intelligent and nonintrusive MELs classification andidentification with relatively high identification accuracy, robustnessand applicability with respect to the diversity of different models ofeach type of MEL. This hybrid classifier 116 employs the power of anSSOM classifier 118 for MELs to first classify the relatively largeamount of MELs models into several clusters. Within each cluster, a moreaccurate identification decision is made by a multi-classone-against-all SVM classifier 120. This hybrid SSOM/SVM classifier 116employs steady-state conditions. For MELs with similar power supplyunits, the disclosed hybrid SSOM/SVM identifier 116 provides hard (orabsolute) decisions. Preferably, soft (or probabilistic) decisionsshould be provided for the electric load identification problem.

While specific embodiments of the disclosed concept have been describedin detail, it will be appreciated by those skilled in the art thatvarious modifications and alternatives to those details could bedeveloped in light of the overall teachings of the disclosure.Accordingly, the particular arrangements disclosed are meant to beillustrative only and not limiting as to the scope of the disclosedconcept which is to be given the full breadth of the claims appended andany and all equivalents thereof.

What is claimed is:
 1. A method of identifying electric load types of aplurality of different electric loads, said method comprising: providinga support vector machine load feature database of a plurality ofdifferent electric load types; sensing a voltage signal and a currentsignal for each of said different electric loads; determining a loadfeature vector including at least six steady-state features with aprocessor from said sensed voltage signal and said sensed currentsignal; and identifying one of said different electric load types byrelating the load feature vector including the at least six steady-statefeatures to the support vector machine load feature database.
 2. Themethod of claim 1 further comprising: training said support vectormachine load feature database as a multi-class one-against-all supportvector machine for each of a plurality of different load classes or foreach of said different electric load types.
 3. The method of claim 2further comprising: employing as said different load classes sevendifferent load classes having distinct steady-state features, said sevendifferent load classes including resistive loads, reactive predominantloads, electronic loads with a power factor correction circuit,electronic loads without a power factor correction circuit, linear powersupplies using a transformer to boost voltage, phase angle controllableloads, and complex structures.
 4. The method of claim 2 furthercomprising: employing said different electric load types having distinctsteady-state features.
 5. The method of claim 1 further comprising:employing a Gaussian radial basis functions kernel with said supportvector machine load feature database.
 6. The method of claim 1 furthercomprising: representing voltage and current waveforms corresponding tosaid sensed voltage signal and said sensed current signal, respectively,by Fourier series.
 7. The method of claim 1 further comprising:employing said at least six steady-state features selected from thegroup consisting of RMS current value, displacement power factor, totalharmonic distortion of current, power factor, current crest factor,current K-factor, admittance, and normalized current third and fifthharmonics.
 8. A method of identifying electric load types of a pluralityof different electric loads, said method comprising: providing adatabase including a first layer formed by a supervised self-organizingmap database and a second layer formed by a support vector machinedatabase; clustering a plurality of different load classes having aplurality of different load features in the first layer; providing aplurality of different electric load types under each of the differentload classes in the second layer; placing different ones of saiddifferent electric load types having similar load feature vectors into asame one of the different load classes; sensing a voltage signal and acurrent signal for each of said different electric loads; determining aload feature vector including a plurality of steady-state features witha processor from said sensed voltage signal and said sensed currentsignal; and identifying by a support vector machine one of saiddifferent electric load types by relating the determined load featurevector including the steady-state features in the second layer of saiddatabase.
 9. The method of claim 8 further comprising: employing as saiddifferent load classes seven different load classes having distinctsteady-state features, said seven different load classes includingresistive loads, reactive predominant loads, electronic loads with apower factor correction circuit, electronic loads without a power factorcorrection circuit, linear power supplies using a transformer to boostvoltage, phase angle controllable loads, and complex structures.
 10. Themethod of claim 8 further comprising: training the supervisedself-organizing map database employing data corresponding to thedifferent electric load types; training the support vector machinedatabase as a multi-class one-against-all support vector machine foreach of said plurality of different load classes; identifying said oneof said different electric load types as being in one of said pluralityof different load classes with said supervised self-organizing mapdatabase; and identifying said one of said different electric load typeswith the trained support vector machine database for said one of saidplurality of different load classes.
 11. The method of claim 10 furthercomprising: extracting information from the trained support vectormachine database and storing simplified information in a trained neurongrid; employing as said different load classes a plurality of differentload categories; determining one of the different load categoriesemploying the determined load feature vector; and employing a supportvector machine discriminator function for each the different loadcategories to identify said one of said different electric load types.12. The method of claim 10 further comprising: employing as saiddifferent load classes seven different load classes having distinctsteady-state features, said seven different load classes includingresistive loads, reactive predominant loads, electronic loads with apower factor correction circuit, electronic loads without a power factorcorrection circuit, linear power supplies using a transformer to boostvoltage, phase angle controllable loads, and complex structures.
 13. Themethod of claim 8 further comprising: representing voltage and currentwaveforms corresponding to said sensed voltage signal and said sensedcurrent signal, respectively, by Fourier series.
 14. The method of claim8 further comprising: selecting the steady-state features of saiddetermined load feature vector from the group consisting of RMS currentvalue, displacement power factor, total harmonic distortion of current,power factor, current crest factor, current K-factor, admittance, andnormalized current third and fifth harmonics.
 15. The method of claim 8further comprising: including with the steady-state features of saiddetermined load feature vector voltage-current trajectory featuresselected from the group consisting of area, eccentricity, and Hausdorffdistance.
 16. A system for identifying electric load types of aplurality of different electric loads, said system comprising: adatabase including a first layer formed by a supervised self-organizingmap database and a second layer formed by a support vector machinedatabase, a plurality of different load classes having a plurality ofdifferent load features being clustered in the first layer, a pluralityof different electric load types being under each of the different loadclasses in the second layer, different ones of said different electricload types having similar load feature vectors being placed into a sameone of the different load classes; a plurality of sensors structured tosense a voltage signal and a current signal for each of said differentelectric loads; and a processor structured to determine a load featurevector including a plurality of steady-state features from said sensedvoltage signal and said sensed current signal, and identify by a supportvector machine one of said different electric load types by relating thedetermined load feature vector including the steady-state features inthe second layer of said database.
 17. The system of claim 16 whereinsaid processor is further structured to train the supervisedself-organizing map database employing data corresponding to thedifferent electric load types, train the support vector machine databaseas a multi-class one-against-all support vector machine for each of saidplurality of different load classes, identify said one of said differentelectric load types as being in one of said plurality of different loadclasses with said supervised self-organizing map database, and identifysaid one of said different electric load types with the trained supportvector machine database for said one of said plurality of different loadclasses.
 18. The system of claim 17 wherein said processor is furtherstructured to extract information from the trained support vectormachine database and store simplified information in a trained neurongrid, employ as said different load classes a plurality of differentload categories; determine one of the different load categoriesemploying the determined load feature vector, and employ a supportvector machine discriminator function for each the different loadcategories to identify said one of said different electric load types.